Lenses.io
Confluent
Striim
Amazon Kinesis
Calisti
Fivetran
Azure Event Hubs
HVR
Spark Streaming
Confluent
Amazon Kinesis
Google Cloud Dataflow
Apache Flink
Leo Platform
Lenses
Striim
Lenses.io delivers DataOps for any self-managed or Cloud Apache Kafka including AWS MSK, Azure HDInsight and Confluent Cloud. Lenses provides self-service platform administration, security, governance and monitoring for Kafka.
Lenses makes engineering teams working with Apache Kafka successful by improving productivity and reducing complexity leading up to 95% faster time to market and reduced operational cost. This means faster and more predictable delivery of strategic real-time projects.
Lenses.io Spark StreamingBased on our record, Lenses.io should be more popular than Spark Streaming. It has been mentiond 6 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
I liked https://lenses.io/ Lots of capabilities but it's not free as I know. Source: about 1 year ago
Currently I'm using Lenses:https://lenses.io/ as UI tool, but while turning on kafka-start-server server.properties and launch the UI on localhost, it failed to connect:. Source: almost 2 years ago
Oh thats sad to hear ... lenses.io is so powerful I am not sure how I would have gotten by to this point without it! Source: about 2 years ago
In Addition to the more critical replys from others, why Kafka still makes sense imo: - you could organist everything with custom pipeline/services at your scale. The cost of maintaining those and keep the technology up to date is growing exponentially with every new Service. Kafka offers ansinge Plattform with a couple components which need to be kept up to date. - deploying, managing and monitoring those service... Source: over 2 years ago
Https://lenses.io/ Kafka UI is pretty solid. Nice to see an open source alternative here. - Source: Hacker News / over 2 years ago
Other stream processing engines (such as Flink and Spark Streaming) provide SQL interfaces too, but the key difference is a streaming database has its storage. Stream processing engines require a dedicated database to store input and output data. On the other hand, streaming databases utilize cloud-native storage to maintain materialized views and states, allowing data replication and independent storage scaling. - Source: dev.to / 3 months ago
Spark Streaming: The component for real-time data processing and analytics. - Source: dev.to / over 1 year ago
Is a big data framework and currently one of the most popular tools for big data analytics. It contains libraries for data analysis, machine learning, graph analysis and streaming live data. In general Spark is faster than Hadoop, as it does not write intermediate results to disk. It is not a data storage system. We can use Spark on top of HDFS or read data from other sources like Amazon S3. It is the designed... - Source: dev.to / over 2 years ago
Confluent - Confluent offers a real-time data platform built around Apache Kafka.
Striim - Striim provides an end-to-end, real-time data integration and streaming analytics platform.
Amazon Kinesis - Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.
Google Cloud Dataflow - Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.
Calisti - Calisti easily manages multiple clusters with a single service mesh manager, each cluster with a synchronized, separate control pane.
Apache Flink - Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.
